Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 696-701.DOI: 10.11772/j.issn.1001-9081.2023030288

• Artificial intelligence • Previous Articles     Next Articles

Relational and interactive graph attention network for aspect-level sentiment analysis

Lei GUO1, Zhen JIA1, Tianrui LI1,2,3()   

  1. 1.School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu Sichuan 611756,China
    2.Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province (Southwest Jiaotong University),Chengdu Sichuan 611756,China
    3.National Engineering Laboratory of Integrated Transportation Big Data Application Technology (Southwest Jiaotong University),Chengdu Sichuan 611756,China
  • Received:2023-03-20 Revised:2023-04-06 Accepted:2023-04-07 Online:2023-05-09 Published:2024-03-10
  • Contact: Tianrui LI
  • About author:GUO Lei, born in 1997, M. S. candidate. His research interests include sentiment analysis, natural language processing.
    JIA Zhen, born in 1975, Ph. D., lecturer. Her research interests include information extraction, knowledge graph.
  • Supported by:
    National Natural Science Foundation of China(62176221)


郭磊1, 贾真1, 李天瑞1,2,3()   

  1. 1.西南交通大学 计算机与人工智能学院,成都 611756
    2.四川省制造业产业链协同与信息化支撑技术重点实验室(西南交通大学),成都 611756
    3.综合交通大数据应用技术国家工程实验室(西南交通大学),成都 611756
  • 通讯作者: 李天瑞
  • 作者简介:郭磊(1997—),男,重庆人,硕士研究生,主要研究方向:情感分析、自然语言处理
  • 基金资助:


The neural network models based on attention mechanism are mainly used in the field of aspect-level sentiment analysis. The dependencies between aspect words and opinion words, as well as the distances between aspect words and context words, are ignored by this type of models, which further leads to inaccurate classification of emotions by this type of models. To solve above problems, a Relational and Interactive Graph ATtention network (RI-GAT) model was established. Firstly, the semantic features of sentences were learned by the Long Short-Term Memory (LSTM) network. Then the learned semantic features were combined with the position information of sentences to generate new features. Finally the dependencies between various aspects words and opinion words were extracted from the new features, realizing efficient and comprehensive use of syntactic dependency information and position information. Experimental results on Laptop, Restaurant, and Twitter datasets show that compared to the suboptimal Dynamic Multi-channel Graph Convolutional Network (DM-GCN), RI-GAT model has the classification Accuracy (Acc) improved by 0.67, 1.65, and 1.36 percentage points, indicating that RI-GAT model can better establish the relationship between aspect words and opinion words, making sentiment classification more accurate.

Key words: aspect-level sentiment analysis, Graph ATtention network (GAT), semantic feature, viewpoint orientation, online comments



关键词: 方面级情感分析, 图注意力网络, 语义特征, 观点倾向, 网络评论

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